Information disclosure, multifaceted collaborative governance, and carbon total factor productivity---an evaluation of the effects of the 'environmental information disclosure pilot' policy based on double machine learning.

• Published in: Journal of Environmental Management
• Main Authors: Wang, Dongmei, Yang, Wenju, Geng, Xiaochen, Li, Qiao
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier Science 2024
• Subjects: Carbon total factor productivity; Causal pathway analysis; Double machine Learing; Doubly Debiased Lasso (DDL); Environmental information disclosure; Multifaceted collaborative government; DML
• Online Access: https://doi.org/10.1016/j.jenvman.2024.121817; https://pubmed.ncbi.nlm.nih.gov/39018840

As an environmental institutional arrangement related to the information factor of the diversified participation of the government, enterprises, the media and the public, the environmental information disclosure pilot policy, can and how to affect the carbon emission efficiency through multiple collaborative governance? This study uses the Environmental Information Disclosure Pilot Policy implemented in China in 2007 as a quasi-natural experiment. It examines 284 prefecture-level cities from 2004 to 2021 and A-share listed companies from 2004 to 2021, constructing an evolutionary game dynamic model involving government, public, enterprises, and media. Through mathematical derivation and assignment analysis, it explores how environmental information impacts carbon emission efficiency under multifaceted collaborative governance, assessing the strategic choices and evolutionary paths of stakeholders before and after policy implementation, using methods like double machine learning for empirical testing. The study highlights several key findings: First, the implementation of the Environmental Information Disclosure Pilot Policy significantly enhanced carbon total factor productivity in pilot cities, as revealed through Double Machine Learning (DML) policy effect evaluation. Second, adjustments for potential estimation biases using Doubly Debiased LASSO (DDL) regression indicated that environmental information disclosure impacts carbon productivity via a governance mechanism involving government, public, media, and enterprises. Third, a causal pathway analysis suggested a sequential logic in governance effectiveness, starting from governmental environmental focus to corporate environmental responsibility. Lastly, integrating DML with a moderation effect model revealed a regulatory role for environmental legislation construction, offering new insights for achieving dual carbon goals and enriching empirical evidence on information's impact on carbon emission efficiency.